Space and Earth monitoring has become a powerful tool for such applications as scientific research and monitoring (e.g., environment and agriculture research and monitoring), weather and natural disaster prediction and monitoring, and global activity monitoring for defense purposes. Space-based (e.g., satellite-based) sensor networks are increasingly being relied on as a powerful technology for such space and Earth monitoring applications. For example, earth and space monitoring missions may encompass a number of small satellites (e.g., satellites of a mass of a few kilograms, or even some which may be of a mass of less than one kilogram), flying in a controlled formation or forming a constellation covering target areas. Typically, such small satellite fleets are deployed as low earth orbit (LEO) satellites which circle the earth in orbits below an altitude of approximately 2,000 kilometers or 1,200 miles, and make one complete revolution around the Earth in about 90 minutes. LEO satellites require less energy to be placed into orbit, and require less powerful amplifiers (e.g., than satellites in geosynchronous orbit at an altitude of approximately 35,786 km or 22,236 miles) for successful transmission.
In such LEO monitoring systems, the satellites travel through their orbits collecting and storing sensor data. In order to deliver the collected data, the satellite must first get into a position where you can communicate with a ground station equipped to deliver the data back to the target facility for processing. While the satellites are traveling through an orbit, however, they cannot always be in contact with a ground station to which they can transfer the collected data. In order to minimize the number of required ground stations, typical systems will deploy the ground stations either near the North Pole or the South Pole, where there is access to many of the LEO satellite orbits. Accordingly, between periods where the satellite is able to contact the ground station upwards of an hour must pass for the satellite to complete an orbit and reach the ground station again. Hence, the sensor satellites experience significant delays between the time data may be collected and the time that the date is able to be delivered to the ground station and on to the target facility for processing. Obviously, especially in situations where real-time data is being collected and analysis similarly should be performed in real time (e.g., in the case of monitoring for severe weather and potential natural disasters, or in the case of monitoring activities that may involve defensive responses), such delays can be detrimental to the efficacy of the monitoring system itself. One solution might be to increase the number of ground stations around the globe, however, that would significantly increase the cost of such systems and would render the logistics of deploying and operating such systems impractical at best and more likely unfeasible.
Additionally, today such LEO satellite-based sensor network systems are deployed as specialized systems consisting of a dedicated infrastructure for each system or mission. As such, each system comprises its own LEO sensing satellites, dedicated ground stations or gateways, dedicated aggregation facilities and dedicated processing facilities. This architecture makes such systems expensive to deploy and maintain. Further, such architectures prevent the ability to utilize common facilities across multiple different space-based sensing systems, and thereby prevent the ability to scale such systems and take advantage of economies of scale. For example, a particular weather sensing system will comprise a fleet of dedicated LEO weather sensing satellites, dedicated gateways for receiving the monitor data from such satellites, and dedicated systems and respective facilities for aggregating, processing and analyzing the gathered data.
What is needed, therefore, is a system architecture and approach to address the challenges of reducing the latency involved in collecting data from space-based sensor satellites, and consolidating the aggregation and processing of data from multiple systems in a general-purpose architecture in order to provide a scalable and more efficient end-to-end (E2E) processing architecture.